Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/121652
Type: Conference item
Title: Green plant segmentation in hyperspectral images using SVM and hyper-hue
Author: Liu, H.
Bruning, B.
Berger, B.
Garnett, T.
Citation: Proceedings: 7th International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS 2019), 2019 / pp.35-36
Issue Date: 2019
Conference Name: International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS) (04 Jul 2019 - 05 Jul 2019 : Lyon, France)
Statement of
Responsibility: 
Huajian Liu, Brooke Bruning, Bettina Berger, Trevor Garnett
Abstract: Green plant segmentation plays an import role in hyperspectral-based plant phenotyping, however, this topic is not given enough consideration. Existing image segmentation methods are dependent on data types, plants and backgrounds and might not utilise the power of hyperspectral data. We proposed a one-class support vector machine classifier combined with a pre-processing method named hyper-hue to segment green plant pixels in hyperspectral images. Experimental results showed that his method can segment green plants from backgrounds with fewer errors and therefore could be used as a general method for hyperspectral-based green plant segmentation.
Rights: Copyright status unknown
RMID: 1000000322
Published version: http://liris.univ-lyon2.fr/IAMPS2019/proceedings/proceedings_IAMPS_2019.pdf
Appears in Collections:Agriculture, Food and Wine publications

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